WS 2013/2014 (310003) Machine Learning: Unsupervised Methods Lecture and Tutorial Prof. Dr. Laurenz Wiskott

Lecture (2 SWS, 2 credit points): Tuesdays 12:00-13:30 in the larger INI seminar room NB 3/57. First time 15.10.2013.
Tutorial (2 SWS, 4 credit points): Tuesdays 10:15-11:45 in the larger INI seminar room NB 3/57. First time 22.10.2013.

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Language: This course can be given in English upon request. Course material (lecture notes and exercise sheets) will be in English in any case.

Goal: (i) The students should get to know a number of unsupervised learning methods. (ii) They should be able to discuss which of the methods might be appropriate for a given data set. (ii) They should understand the mathematics of these methods.

Content: This course covers a variety of unsupervised methods from machine learning such as principal component analysis, independent component analysis, vector quantization, clustering, self-organizing maps, growing neural gas, Bayesian theory and graphical models. We will also briefly discuss reinforcement learning.

Requirements: The mathematical level of the course is mixed but generally high. The tutorial is almost entirely mathematical. Mathematics required include calculus (functions, derivatives, integrals, differential equations, ...), linear algebra (vectors, matrices, inner product, orthogonal vectors, basis systems, ...), and a bit of probability theory (probabilities, probability densities, Bayes' theorem, ...).

Exam: The course has been concluded with an oral exam. The dates have been set in the last lecture. There were two question and answer sessions, 2014-02-24 Mon 14:00-15:00 and 2014-03-21 Fri 15:00-16:00.

Schedule